00_loadData.R
isabellaoleksy
2022-07-11
Read in data from Sky Pond (alpine) and The Loch (subalpine)
Sky littoral sites
SB1 <- read_csv(here("data/sky/littoral/SB1_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))
SB2 <- read_csv(here("data/sky/littoral/SB2_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))
SB3 <- read_csv(here("data/sky/littoral/SB3_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))
SB4 <- read_csv(here("data/sky/littoral/SB4_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))
SB5 <- read_csv(here("data/sky/littoral/SB5_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))Loch littoral sites
LB1 <- read_csv(here("data/loch/littoral/LochInlet_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))
LB3 <- read_csv(here("data/loch/littoral/LB3_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))
LB4 <- read_csv(here("data/loch/littoral/LB4_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))
LB5 <- read_csv(here("data/loch/littoral/LB5_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))
LB6 <- read_csv(here("data/loch/littoral/LB6_2016.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))Master littoral
sky_littoral <- bind_rows(SB1, SB2, SB3, SB4, SB5) %>%
mutate(habitat = "littoral")
loch_littoral <- bind_rows(LB3, LB4, LB5, LB6, LB1) %>%
mutate(habitat = "littoral")
littoral_master <- bind_rows(sky_littoral,
loch_littoral) %>%
mutate(dateTime = round_date(dateTime, "hour")) #For making joining to pelagic data easierSky buoy temperature 2016
# sky_buoy_long_original <-
# read.table(here("data/sky/pelagic/sky_2016_tempProfile.txt"),
# sep = ",",
# header = TRUE) %>%
# mutate(
# dateTime = ymd_hms(dateTime),
# dateTime = with_tz(dateTime, tz = "America/Denver"),
# dateTime = force_tz(dateTime, "America/Denver")
# ) %>%
# # filter(dateTime >= "2016-06-13" &
# # dateTime <= "2016-10-30") %>% #ice off and on dates
# rename(wtr_6.5 = wtr_7.0) %>%
# pivot_longer(-dateTime, names_to = "depth") %>%
# mutate(habitat = "pelagic") %>%
# separate(col = depth,
# into = c("parameter", "depth"),
# sep = "_") %>%
# mutate(parameter = "temperature",
# lakeID = "SkyPond")
#
## Updated Sky Pond 0.5m and 6.5m miniDot data from 2016 through 2018
# surface <- read_csv(here("data/sky/pelagic/sky_0.5m_temp_DO.csv"))%>%
# mutate(dateTime= mdy_hm(dateTime)) %>%
# pivot_longer(temp_0.5:DO_0.5,
# names_to = c("parameter","depth"),
# names_sep = "_",
# values_to = "value")
# hypo <- read_csv(here("data/sky/pelagic/sky_6.5m_temp_DO.csv"))%>%
# mutate(dateTime= mdy_hm(dateTime)) %>%
# pivot_longer(temp_6.5:DO_6.5,
# names_to = c("parameter","depth"),
# names_sep = "_",
# values_to = "value")
# new_sky <- bind_rows(surface, hypo) %>%
# mutate(dateTime = with_tz(dateTime, tz = "America/Denver"),
# dateTime = force_tz(dateTime, "America/Denver"),
# dateTime=round_date(dateTime, "30 min"))
#
# sky_buoy_long <- new_sky %>%
# bind_rows(., sky_buoy_long_original %>%
# select(-habitat) %>%
# mutate(parameter=recode(parameter, temperature="temp"),
# dateTime=round_date(dateTime, "30 min"))%>%
# filter(!dateTime %in% new_sky$dateTime))
# min(sky_buoy_long$dateTime)
# max(sky_buoy_long$dateTime)
# write_csv(sky_buoy_long, here("data/sky_buoy_long_temp_DO_2016-07-14_to_2018-08-25.csv"))Read in Sky Pond buoy data 2016-07-14 20:30:00 UTC through 2018-08-25 09:00:00 UTC I commented out the wrangling I did above to combine a few .csv files
sky_buoy_long<-read_csv(here("data/sky_buoy_long_temp_DO_2016-07-14_to_2018-08-25.csv")) %>%
mutate(dateTime = with_tz(dateTime, tz = "America/Denver"),
dateTime = force_tz(dateTime, "America/Denver"),
depth = as.character(as.numeric(depth)))##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## lakeID = col_character(),
## dateTime = col_datetime(format = ""),
## parameter = col_character(),
## depth = col_double(),
## value = col_double()
## )- Note that 0.5 and 6.5 are MiniDOT observations which are more frequent than the others
- All other depths were recorded with iButtons, same as the littoral zone sites # Loch buoy temperature
# loch_buoy_long_original <- read.table(here("data/loch/pelagic/loch_2016_tempProfile.txt"), sep=",", header=TRUE) %>%
# mutate(dateTime = ymd_hms(as.factor(dateTime)),
# dateTime = force_tz(dateTime, tz="America/Denver"),
# dateTime = with_tz(dateTime, "America/Denver")) %>%
# filter(dateTime > "2016-05-31" & dateTime <= "2016-10-30") %>% #ice off and on dates
# pivot_longer(-dateTime, names_to="depth") %>%
# mutate(habitat="pelagic") %>%
# separate(col = depth, into = c("parameter", "depth"), sep = "_") %>%
# mutate(parameter="temperature",
# lakeID="TheLoch")
#
## Updated The Loch 0.5m and 4.5m miniDot data from 2016 through 2018
# surface <- read_csv(here("data/loch/pelagic/loch_0.5m_temp_DO.csv"))%>%
# mutate(dateTime= mdy_hm(dateTime)) %>%
# pivot_longer(temp_0.5:DO_0.5,
# names_to = c("parameter","depth"),
# names_sep = "_",
# values_to = "value")
# hypo <- read_csv(here("data/loch/pelagic/loch_4.5m_temp_DO.csv"))%>%
# mutate(dateTime= mdy_hm(dateTime)) %>%
# pivot_longer(temp_4.5:DO_4.5,
# names_to = c("parameter","depth"),
# names_sep = "_",
# values_to = "value")
# new_loch <- bind_rows(surface, hypo) %>%
# mutate(dateTime = with_tz(dateTime, tz = "America/Denver"),
# dateTime = force_tz(dateTime, "America/Denver"),
# dateTime=round_date(dateTime, "30 min"))
#
# loch_buoy_long <- new_loch %>%
# bind_rows(., loch_buoy_long_original %>%
# select(-habitat) %>%
# mutate(parameter=recode(parameter, temperature="temp"),
# dateTime=round_date(dateTime, "30 min"))%>%
# filter(!dateTime %in% new_loch$dateTime))
# min(loch_buoy_long$dateTime)
# max(loch_buoy_long$dateTime)
# write_csv(loch_buoy_long, here("data/loch_buoy_long_temp_DO_2016-07-19_to_2018-09-11.csv"))Read in Sky Pond buoy data 2016-07-14 20:30:00 UTC through 2018-08-25 09:00:00 UTC I commented out the wrangling I did above to combine a few .csv files
loch_buoy_long<-read_csv(here("data/loch_buoy_long_temp_DO_2016-07-19_to_2018-09-11.csv")) %>%
mutate(dateTime = with_tz(dateTime, tz = "America/Denver"),
dateTime = force_tz(dateTime, "America/Denver"),
depth = as.character(as.numeric(depth)))##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## lakeID = col_character(),
## dateTime = col_datetime(format = ""),
## parameter = col_character(),
## depth = col_double(),
## value = col_double()
## )Master buoy df
buoy_master <-
bind_rows(loch_buoy_long, sky_buoy_long) %>%
mutate(
depth_category = case_when(depth == 0.5 ~ "surface",
depth == 4.5 | depth == 6.5 ~ "bottom",
depth %in% c("2","4","4") ~ depth,
TRUE ~ "other")
# depth_category = factor(depth_category,
# levels = c("surface", "bottom"))
) Meterological data
wx <- read_csv(here("data/WY2016to2022_LochVale_WX.csv")) %>%
mutate(dateTime = mdy_hm(dateTime))##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## dateTime = col_character(),
## T_air = col_double(),
## RH = col_double(),
## WDir = col_double(),
## WSpd = col_double(),
## WGust = col_double(),
## SWin = col_logical(),
## SWout = col_logical(),
## LWin = col_logical(),
## LWout = col_logical(),
## measurement_height = col_double(),
## station_name = col_character()
## )## Warning: 56262 parsing failures.
## row col expected actual file
## 34921 SWin 1/0/T/F/TRUE/FALSE -1 '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## 34921 SWout 1/0/T/F/TRUE/FALSE 0.036 '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## 34921 LWin 1/0/T/F/TRUE/FALSE 210 '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## 34921 LWout 1/0/T/F/TRUE/FALSE 226 '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## 34922 SWin 1/0/T/F/TRUE/FALSE -1 '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## ..... ..... .................. ...... ..................................................................................................
## See problems(...) for more details.Chlorophyll data
chl <- read_csv(here("data/AllPelagicCHLA_2016-2021.csv")) %>%
mutate(date= mdy(date))##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## siteID = col_character(),
## sampleType = col_character(),
## date = col_character(),
## year = col_double(),
## lakeID = col_character(),
## easting = col_double(),
## northing = col_double(),
## chla_ugL = col_double()
## )#Sky time series 2016-2018 including under-ice data
Some kind of crazy dynamics going on around ice-off. Interesting that winter 2016-2017 the lake pretty distinctly reverse stratifes, but not in 2017-2018 in 2017, The Loch (subalpine lakes ~200m lower in elevation) had ice-off by 5/16. Usually Sky Pond is ~2 weeks or a little more behind.
In 2018, ice-off occured in The Loch on 5/15, similar to the previous year. Funky DO dynamics in Sky Pond likely shortly after ice off. Lake appears to be well-mixed though.
#Loch time series 2016-2018 including under-ice data
Also some kind of crazy dynamics going on around ice-off. in 2017, The Loch had ice-off by 5/16.
Some wild DO dynamics in late april early may ahead of ice-out.
in 2018 even after ice-off we have periods of anoxia.
Data vis: temperature from littoral zone —————————————————————-
How do the littoral zone temperatures compared to 0.5m temperatures? Include 0.5m depth in the background of each panel
How do the littoral zone diel fluctuations compare to 0.5m temperatures? Include 0.5m depth in the background of each panel
Chlorophyll data
Not entirely sure why there is a gap in 2017 since I know I was collecting data during this time (IAO)
Met data
How do the various meterological variables vary by year? ## Year-round ata
